Overview

Dataset statistics

Number of variables27
Number of observations112647
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory130.3 MiB
Average record size in memory1.2 KiB

Variable types

Numeric9
Categorical17
DateTime1

Alerts

order_id has a high cardinality: 98665 distinct values High cardinality
product_id has a high cardinality: 32951 distinct values High cardinality
seller_id has a high cardinality: 3095 distinct values High cardinality
seller_city has a high cardinality: 611 distinct values High cardinality
product_category_name_english has a high cardinality: 72 distinct values High cardinality
customer_id has a high cardinality: 98665 distinct values High cardinality
order_delivery_date has a high cardinality: 96049 distinct values High cardinality
order_estimated_delivery_date has a high cardinality: 449 distinct values High cardinality
payment_type has a high cardinality: 74 distinct values High cardinality
customer_unique_id has a high cardinality: 95419 distinct values High cardinality
customer_city has a high cardinality: 4110 distinct values High cardinality
order_year is highly correlated with order_monthHigh correlation
order_month is highly correlated with order_yearHigh correlation
order_year is highly correlated with order_monthHigh correlation
order_month is highly correlated with order_yearHigh correlation
seller_zip_code is highly correlated with seller_state and 1 other fieldsHigh correlation
seller_state is highly correlated with seller_zip_code and 1 other fieldsHigh correlation
product_category_name_english is highly correlated with seller_zip_code and 1 other fieldsHigh correlation
customer_zip_code is highly correlated with customer_stateHigh correlation
customer_state is highly correlated with customer_zip_codeHigh correlation
order_year is highly correlated with order_monthHigh correlation
order_month is highly correlated with order_yearHigh correlation
df_index is uniformly distributed Uniform
order_id is uniformly distributed Uniform
customer_id is uniformly distributed Uniform
order_delivery_date is uniformly distributed Uniform
customer_unique_id is uniformly distributed Uniform
df_index has unique values Unique

Reproduction

Analysis started2021-12-17 18:52:44.305805
Analysis finished2021-12-17 18:53:30.110107
Duration45.8 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

UNIFORM
UNIQUE

Distinct112647
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56323.75256
Minimum0
Maximum112649
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size880.2 KiB
2021-12-17T13:53:30.464363image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5632.3
Q128161.5
median56323
Q384487.5
95-th percentile107016.7
Maximum112649
Range112649
Interquartile range (IQR)56326

Descriptive statistics

Standard deviation32519.50873
Coefficient of variation (CV)0.5773675804
Kurtosis-1.199991297
Mean56323.75256
Median Absolute Deviation (MAD)28163
Skewness5.183415282 × 10-5
Sum6344701755
Variance1057518448
MonotonicityStrictly increasing
2021-12-17T13:53:30.743165image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
750941
 
< 0.1%
751051
 
< 0.1%
751041
 
< 0.1%
751031
 
< 0.1%
751021
 
< 0.1%
751011
 
< 0.1%
751001
 
< 0.1%
750991
 
< 0.1%
750981
 
< 0.1%
Other values (112637)112637
> 99.9%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
1126491
< 0.1%
1126481
< 0.1%
1126471
< 0.1%
1126461
< 0.1%
1126451
< 0.1%
1126441
< 0.1%
1126431
< 0.1%
1126421
< 0.1%
1126411
< 0.1%
1126401
< 0.1%

order_id
Categorical

HIGH CARDINALITY
UNIFORM

Distinct98665
Distinct (%)87.6%
Missing0
Missing (%)0.0%
Memory size9.6 MiB
8272b63d03f5f79c56e9e4120aec44ef
 
21
ab14fdcfbe524636d65ee38360e22ce8
 
20
1b15974a0141d54e36626dca3fdc731a
 
20
9ef13efd6949e4573a18964dd1bbe7f5
 
15
428a2f660dc84138d969ccd69a0ab6d5
 
15
Other values (98660)
112556 

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique88863 ?
Unique (%)78.9%

Sample

1st row00010242fe8c5a6d1ba2dd792cb16214
2nd row00018f77f2f0320c557190d7a144bdd3
3rd row000229ec398224ef6ca0657da4fc703e
4th row00024acbcdf0a6daa1e931b038114c75
5th row00042b26cf59d7ce69dfabb4e55b4fd9

Common Values

ValueCountFrequency (%)
8272b63d03f5f79c56e9e4120aec44ef21
 
< 0.1%
ab14fdcfbe524636d65ee38360e22ce820
 
< 0.1%
1b15974a0141d54e36626dca3fdc731a20
 
< 0.1%
9ef13efd6949e4573a18964dd1bbe7f515
 
< 0.1%
428a2f660dc84138d969ccd69a0ab6d515
 
< 0.1%
73c8ab38f07dc94389065f7eba4f297a14
 
< 0.1%
9bdc4d4c71aa1de4606060929dee888c14
 
< 0.1%
37ee401157a3a0b28c9c6d0ed8c3b24b13
 
< 0.1%
c05d6a79e55da72ca780ce90364abed912
 
< 0.1%
3a213fcdfe7d98be74ea0dc05a8b31ae12
 
< 0.1%
Other values (98655)112491
99.9%

Length

2021-12-17T13:53:30.898508image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
8272b63d03f5f79c56e9e4120aec44ef21
 
< 0.1%
1b15974a0141d54e36626dca3fdc731a20
 
< 0.1%
ab14fdcfbe524636d65ee38360e22ce820
 
< 0.1%
9ef13efd6949e4573a18964dd1bbe7f515
 
< 0.1%
428a2f660dc84138d969ccd69a0ab6d515
 
< 0.1%
73c8ab38f07dc94389065f7eba4f297a14
 
< 0.1%
9bdc4d4c71aa1de4606060929dee888c14
 
< 0.1%
37ee401157a3a0b28c9c6d0ed8c3b24b13
 
< 0.1%
2c2a19b5703863c908512d135aa6accc12
 
< 0.1%
af822dacd6f5cff7376413c03a388bb712
 
< 0.1%
Other values (98655)112491
99.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

order_item_id
Real number (ℝ≥0)

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.197812636
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size880.2 KiB
2021-12-17T13:53:31.024301image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum21
Range20
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.7051086788
Coefficient of variation (CV)0.5886635837
Kurtosis103.8700387
Mean1.197812636
Median Absolute Deviation (MAD)0
Skewness7.580982582
Sum134930
Variance0.497178249
MonotonicityNot monotonic
2021-12-17T13:53:31.163524image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
198665
87.6%
29802
 
8.7%
32286
 
2.0%
4965
 
0.9%
5460
 
0.4%
6256
 
0.2%
758
 
0.1%
836
 
< 0.1%
928
 
< 0.1%
1025
 
< 0.1%
Other values (11)66
 
0.1%
ValueCountFrequency (%)
198665
87.6%
29802
 
8.7%
32286
 
2.0%
4965
 
0.9%
5460
 
0.4%
6256
 
0.2%
758
 
0.1%
836
 
< 0.1%
928
 
< 0.1%
1025
 
< 0.1%
ValueCountFrequency (%)
211
 
< 0.1%
203
 
< 0.1%
193
 
< 0.1%
183
 
< 0.1%
173
 
< 0.1%
163
 
< 0.1%
155
 
< 0.1%
147
< 0.1%
138
< 0.1%
1213
< 0.1%

product_id
Categorical

HIGH CARDINALITY

Distinct32951
Distinct (%)29.3%
Missing0
Missing (%)0.0%
Memory size9.6 MiB
aca2eb7d00ea1a7b8ebd4e68314663af
 
527
99a4788cb24856965c36a24e339b6058
 
488
422879e10f46682990de24d770e7f83d
 
484
389d119b48cf3043d311335e499d9c6b
 
392
368c6c730842d78016ad823897a372db
 
388
Other values (32946)
110368 

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18118 ?
Unique (%)16.1%

Sample

1st row4244733e06e7ecb4970a6e2683c13e61
2nd rowe5f2d52b802189ee658865ca93d83a8f
3rd rowc777355d18b72b67abbeef9df44fd0fd
4th row7634da152a4610f1595efa32f14722fc
5th rowac6c3623068f30de03045865e4e10089

Common Values

ValueCountFrequency (%)
aca2eb7d00ea1a7b8ebd4e68314663af527
 
0.5%
99a4788cb24856965c36a24e339b6058488
 
0.4%
422879e10f46682990de24d770e7f83d484
 
0.4%
389d119b48cf3043d311335e499d9c6b392
 
0.3%
368c6c730842d78016ad823897a372db388
 
0.3%
53759a2ecddad2bb87a079a1f1519f73373
 
0.3%
d1c427060a0f73f6b889a5c7c61f2ac4343
 
0.3%
53b36df67ebb7c41585e8d54d6772e08323
 
0.3%
154e7e31ebfa092203795c972e5804a6281
 
0.2%
3dd2a17168ec895c781a9191c1e95ad7274
 
0.2%
Other values (32941)108774
96.6%

Length

2021-12-17T13:53:31.313985image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
aca2eb7d00ea1a7b8ebd4e68314663af527
 
0.5%
99a4788cb24856965c36a24e339b6058488
 
0.4%
422879e10f46682990de24d770e7f83d484
 
0.4%
389d119b48cf3043d311335e499d9c6b392
 
0.3%
368c6c730842d78016ad823897a372db388
 
0.3%
53759a2ecddad2bb87a079a1f1519f73373
 
0.3%
d1c427060a0f73f6b889a5c7c61f2ac4343
 
0.3%
53b36df67ebb7c41585e8d54d6772e08323
 
0.3%
154e7e31ebfa092203795c972e5804a6281
 
0.2%
3dd2a17168ec895c781a9191c1e95ad7274
 
0.2%
Other values (32941)108774
96.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

seller_id
Categorical

HIGH CARDINALITY

Distinct3095
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size9.6 MiB
6560211a19b47992c3666cc44a7e94c0
 
2033
4a3ca9315b744ce9f8e9374361493884
 
1987
1f50f920176fa81dab994f9023523100
 
1931
cc419e0650a3c5ba77189a1882b7556a
 
1775
da8622b14eb17ae2831f4ac5b9dab84a
 
1551
Other values (3090)
103370 

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique509 ?
Unique (%)0.5%

Sample

1st row48436dade18ac8b2bce089ec2a041202
2nd rowdd7ddc04e1b6c2c614352b383efe2d36
3rd row5b51032eddd242adc84c38acab88f23d
4th row9d7a1d34a5052409006425275ba1c2b4
5th rowdf560393f3a51e74553ab94004ba5c87

Common Values

ValueCountFrequency (%)
6560211a19b47992c3666cc44a7e94c02033
 
1.8%
4a3ca9315b744ce9f8e93743614938841987
 
1.8%
1f50f920176fa81dab994f90235231001931
 
1.7%
cc419e0650a3c5ba77189a1882b7556a1775
 
1.6%
da8622b14eb17ae2831f4ac5b9dab84a1551
 
1.4%
955fee9216a65b617aa5c0531780ce601499
 
1.3%
1025f0e2d44d7041d6cf58b6550e0bfa1428
 
1.3%
7c67e1448b00f6e969d365cea6b010ab1364
 
1.2%
ea8482cd71df3c1969d7b9473ff13abc1203
 
1.1%
7a67c85e85bb2ce8582c35f2203ad7361171
 
1.0%
Other values (3085)96705
85.8%

Length

2021-12-17T13:53:31.437006image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
6560211a19b47992c3666cc44a7e94c02033
 
1.8%
4a3ca9315b744ce9f8e93743614938841987
 
1.8%
1f50f920176fa81dab994f90235231001931
 
1.7%
cc419e0650a3c5ba77189a1882b7556a1775
 
1.6%
da8622b14eb17ae2831f4ac5b9dab84a1551
 
1.4%
955fee9216a65b617aa5c0531780ce601499
 
1.3%
1025f0e2d44d7041d6cf58b6550e0bfa1428
 
1.3%
7c67e1448b00f6e969d365cea6b010ab1364
 
1.2%
ea8482cd71df3c1969d7b9473ff13abc1203
 
1.1%
7a67c85e85bb2ce8582c35f2203ad7361171
 
1.0%
Other values (3085)96705
85.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

seller_zip_code
Real number (ℝ≥0)

HIGH CORRELATION

Distinct2246
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24437.64254
Minimum1001
Maximum99730
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size880.2 KiB
2021-12-17T13:53:31.586850image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1001
5-th percentile2969
Q16429
median13568
Q327930
95-th percentile88330
Maximum99730
Range98729
Interquartile range (IQR)21501

Descriptive statistics

Standard deviation27594.81009
Coefficient of variation (CV)1.129192803
Kurtosis0.9332621576
Mean24437.64254
Median Absolute Deviation (MAD)8038
Skewness1.555585514
Sum2752827119
Variance761473543.8
MonotonicityNot monotonic
2021-12-17T13:53:31.970879image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
149407750
 
6.9%
58492047
 
1.8%
150252008
 
1.8%
90151781
 
1.6%
134051567
 
1.4%
47821518
 
1.3%
85771456
 
1.3%
32041428
 
1.3%
41601229
 
1.1%
132321195
 
1.1%
Other values (2236)90668
80.5%
ValueCountFrequency (%)
100119
 
< 0.1%
102139
 
< 0.1%
10225
 
< 0.1%
10235
 
< 0.1%
1026303
0.3%
1031124
0.1%
103517
 
< 0.1%
10391
 
< 0.1%
104024
 
< 0.1%
10412
 
< 0.1%
ValueCountFrequency (%)
9973012
 
< 0.1%
997002
 
< 0.1%
996701
 
< 0.1%
9950057
0.1%
993002
 
< 0.1%
9897521
 
< 0.1%
989202
 
< 0.1%
9891013
 
< 0.1%
9880362
0.1%
987804
 
< 0.1%

seller_city
Categorical

HIGH CARDINALITY

Distinct611
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size7.2 MiB
sao paulo
27983 
ibitinga
7750 
curitiba
 
3013
santo andre
 
2964
belo horizonte
 
2593
Other values (606)
68344 

Length

Max length40
Median length9
Mean length10.10200893
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique68 ?
Unique (%)0.1%

Sample

1st rowvolta redonda
2nd rowsao paulo
3rd rowborda da mata
4th rowfranca
5th rowloanda

Common Values

ValueCountFrequency (%)
sao paulo27983
24.8%
ibitinga7750
 
6.9%
curitiba3013
 
2.7%
santo andre2964
 
2.6%
belo horizonte2593
 
2.3%
sao jose do rio preto2579
 
2.3%
rio de janeiro2442
 
2.2%
guarulhos2362
 
2.1%
ribeirao preto2269
 
2.0%
maringa2220
 
2.0%
Other values (601)56472
50.1%

Length

2021-12-17T13:53:32.158495image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sao34728
 
17.9%
paulo28233
 
14.6%
ibitinga7750
 
4.0%
rio5702
 
2.9%
do5291
 
2.7%
preto5287
 
2.7%
de4020
 
2.1%
jose3904
 
2.0%
santo3081
 
1.6%
curitiba3013
 
1.6%
Other values (640)92660
47.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

seller_state
Categorical

HIGH CORRELATION

Distinct23
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.3 MiB
SP
80342 
MG
8827 
PR
8668 
RJ
 
4818
SC
 
4075
Other values (18)
 
5917

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowSP
2nd rowSP
3rd rowMG
4th rowSP
5th rowPR

Common Values

ValueCountFrequency (%)
SP80342
71.3%
MG8827
 
7.8%
PR8668
 
7.7%
RJ4818
 
4.3%
SC4075
 
3.6%
RS2199
 
2.0%
DF899
 
0.8%
BA643
 
0.6%
GO520
 
0.5%
PE448
 
0.4%
Other values (13)1208
 
1.1%

Length

2021-12-17T13:53:32.310497image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sp80342
71.3%
mg8827
 
7.8%
pr8668
 
7.7%
rj4818
 
4.3%
sc4075
 
3.6%
rs2199
 
2.0%
df899
 
0.8%
ba643
 
0.6%
go520
 
0.5%
pe448
 
0.4%
Other values (13)1208
 
1.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

product_category_name_english
Categorical

HIGH CARDINALITY
HIGH CORRELATION

Distinct72
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.5 MiB
bed_bath_table
11115 
health_beauty
9667 
sports_leisure
8641 
furniture_decor
8334 
computers_accessories
7827 
Other values (67)
67063 

Length

Max length39
Median length13
Mean length12.94820989
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcool_stuff
2nd rowpet_shop
3rd rowfurniture_decor
4th rowperfumery
5th rowgarden_tools

Common Values

ValueCountFrequency (%)
bed_bath_table11115
 
9.9%
health_beauty9667
 
8.6%
sports_leisure8641
 
7.7%
furniture_decor8334
 
7.4%
computers_accessories7827
 
6.9%
housewares6964
 
6.2%
watches_gifts5991
 
5.3%
telephony4545
 
4.0%
garden_tools4347
 
3.9%
auto4235
 
3.8%
Other values (62)40981
36.4%

Length

2021-12-17T13:53:32.467761image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
bed_bath_table11115
 
9.7%
health_beauty9667
 
8.5%
sports_leisure8641
 
7.6%
furniture_decor8334
 
7.3%
computers_accessories7827
 
6.8%
housewares6964
 
6.1%
watches_gifts5991
 
5.2%
telephony4545
 
4.0%
garden_tools4347
 
3.8%
auto4235
 
3.7%
Other values (63)42608
37.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

customer_id
Categorical

HIGH CARDINALITY
UNIFORM

Distinct98665
Distinct (%)87.6%
Missing0
Missing (%)0.0%
Memory size9.6 MiB
fc3d1daec319d62d49bfb5e1f83123e9
 
21
bd5d39761aa56689a265d95d8d32b8be
 
20
be1b70680b9f9694d8c70f41fa3dc92b
 
20
adb32467ecc74b53576d9d13a5a55891
 
15
10de381f8a8d23fff822753305f71cae
 
15
Other values (98660)
112556 

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique88863 ?
Unique (%)78.9%

Sample

1st row3ce436f183e68e07877b285a838db11a
2nd rowf6dd3ec061db4e3987629fe6b26e5cce
3rd row6489ae5e4333f3693df5ad4372dab6d3
4th rowd4eb9395c8c0431ee92fce09860c5a06
5th row58dbd0b2d70206bf40e62cd34e84d795

Common Values

ValueCountFrequency (%)
fc3d1daec319d62d49bfb5e1f83123e921
 
< 0.1%
bd5d39761aa56689a265d95d8d32b8be20
 
< 0.1%
be1b70680b9f9694d8c70f41fa3dc92b20
 
< 0.1%
adb32467ecc74b53576d9d13a5a5589115
 
< 0.1%
10de381f8a8d23fff822753305f71cae15
 
< 0.1%
d5f2b3f597c7ccafbb5cac0bcc3d602414
 
< 0.1%
a7693fba2ff9583c78751f2b66ecab9d14
 
< 0.1%
7d321bd4e8ba1caf74c4c1aabd9ae52413
 
< 0.1%
3b54b5978e9ace64a63f90d176ffb15812
 
< 0.1%
91f92cfee46b79581b05aa974dd57ce512
 
< 0.1%
Other values (98655)112491
99.9%

Length

2021-12-17T13:53:32.625996image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
fc3d1daec319d62d49bfb5e1f83123e921
 
< 0.1%
be1b70680b9f9694d8c70f41fa3dc92b20
 
< 0.1%
bd5d39761aa56689a265d95d8d32b8be20
 
< 0.1%
adb32467ecc74b53576d9d13a5a5589115
 
< 0.1%
10de381f8a8d23fff822753305f71cae15
 
< 0.1%
d5f2b3f597c7ccafbb5cac0bcc3d602414
 
< 0.1%
a7693fba2ff9583c78751f2b66ecab9d14
 
< 0.1%
7d321bd4e8ba1caf74c4c1aabd9ae52413
 
< 0.1%
0d93f21f3e8543a9d0d8ece01561f5b212
 
< 0.1%
9eb3d566e87289dcb0acf28e1407c83912
 
< 0.1%
Other values (98655)112491
99.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

order_status
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.1 MiB
delivered
110194 
shipped
 
1185
canceled
 
542
invoiced
 
359
processing
 
357
Other values (2)
 
10

Length

Max length11
Median length9
Mean length8.974229229
Min length7

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdelivered
2nd rowdelivered
3rd rowdelivered
4th rowdelivered
5th rowdelivered

Common Values

ValueCountFrequency (%)
delivered110194
97.8%
shipped1185
 
1.1%
canceled542
 
0.5%
invoiced359
 
0.3%
processing357
 
0.3%
unavailable7
 
< 0.1%
approved3
 
< 0.1%

Length

2021-12-17T13:53:32.771174image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-17T13:53:32.880885image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
delivered110194
97.8%
shipped1185
 
1.1%
canceled542
 
0.5%
invoiced359
 
0.3%
processing357
 
0.3%
unavailable7
 
< 0.1%
approved3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct98111
Distinct (%)87.1%
Missing0
Missing (%)0.0%
Memory size880.2 KiB
Minimum2016-09-04 21:15:19
Maximum2018-09-03 09:06:57
2021-12-17T13:53:33.025288image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T13:53:33.195170image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

order_delivery_date
Categorical

HIGH CARDINALITY
UNIFORM

Distinct96049
Distinct (%)85.3%
Missing0
Missing (%)0.0%
Memory size8.2 MiB
2017-07-31 18:03:02
 
21
2018-08-13 00:00:00
 
20
2017-12-01 00:00:00
 
20
2018-03-05 15:22:27
 
20
2017-09-02 12:13:03
 
20
Other values (96044)
112546 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique85406 ?
Unique (%)75.8%

Sample

1st row2017-09-20 23:43:48
2nd row2017-05-12 16:04:24
3rd row2018-01-22 13:19:16
4th row2018-08-14 13:32:39
5th row2017-03-01 16:42:31

Common Values

ValueCountFrequency (%)
2017-07-31 18:03:0221
 
< 0.1%
2018-08-13 00:00:0020
 
< 0.1%
2017-12-01 00:00:0020
 
< 0.1%
2018-03-05 15:22:2720
 
< 0.1%
2017-09-02 12:13:0320
 
< 0.1%
2018-08-10 00:00:0018
 
< 0.1%
2018-04-12 00:00:0017
 
< 0.1%
2018-03-13 00:00:0017
 
< 0.1%
2017-12-05 00:00:0017
 
< 0.1%
2018-02-06 00:00:0017
 
< 0.1%
Other values (96039)112460
99.8%

Length

2021-12-17T13:53:33.368936image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
00:00:002454
 
1.1%
2018-05-14524
 
0.2%
2018-08-13521
 
0.2%
2018-05-21508
 
0.2%
2018-08-27501
 
0.2%
2018-05-18494
 
0.2%
2018-04-11482
 
0.2%
2017-12-11480
 
0.2%
2018-05-03476
 
0.2%
2017-06-19473
 
0.2%
Other values (41745)218381
96.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

order_estimated_delivery_date
Categorical

HIGH CARDINALITY

Distinct449
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size8.2 MiB
2017-12-20 00:00:00
 
604
2018-05-29 00:00:00
 
598
2018-03-12 00:00:00
 
592
2018-03-13 00:00:00
 
584
2018-07-05 00:00:00
 
571
Other values (444)
109698 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18 ?
Unique (%)< 0.1%

Sample

1st row2017-09-29 00:00:00
2nd row2017-05-15 00:00:00
3rd row2018-02-05 00:00:00
4th row2018-08-20 00:00:00
5th row2017-03-17 00:00:00

Common Values

ValueCountFrequency (%)
2017-12-20 00:00:00604
 
0.5%
2018-05-29 00:00:00598
 
0.5%
2018-03-12 00:00:00592
 
0.5%
2018-03-13 00:00:00584
 
0.5%
2018-07-05 00:00:00571
 
0.5%
2018-05-28 00:00:00563
 
0.5%
2017-12-19 00:00:00563
 
0.5%
2018-02-14 00:00:00562
 
0.5%
2017-12-18 00:00:00562
 
0.5%
2018-05-30 00:00:00559
 
0.5%
Other values (439)106889
94.9%

Length

2021-12-17T13:53:33.496757image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
00:00:00112647
50.0%
2017-12-20604
 
0.3%
2018-05-29598
 
0.3%
2018-03-12592
 
0.3%
2018-03-13584
 
0.3%
2018-07-05571
 
0.3%
2018-05-28563
 
0.2%
2017-12-19563
 
0.2%
2018-02-14562
 
0.2%
2017-12-18562
 
0.2%
Other values (440)107448
47.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

review_score
Real number (ℝ≥0)

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.016149269
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size880.2 KiB
2021-12-17T13:53:33.618026image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13.5
median5
Q35
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation1.398448033
Coefficient of variation (CV)0.3482061895
Kurtosis0.1239715653
Mean4.016149269
Median Absolute Deviation (MAD)0
Skewness-1.246036083
Sum452407.1667
Variance1.955656902
MonotonicityNot monotonic
2021-12-17T13:53:33.751469image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
563180
56.1%
421229
 
18.8%
114626
 
13.0%
39502
 
8.4%
23941
 
3.5%
4.569
 
0.1%
2.548
 
< 0.1%
3.532
 
< 0.1%
1.518
 
< 0.1%
3.3333333331
 
< 0.1%
ValueCountFrequency (%)
114626
13.0%
1.518
 
< 0.1%
23941
 
3.5%
2.548
 
< 0.1%
39502
8.4%
3.3333333331
 
< 0.1%
3.532
 
< 0.1%
421229
18.8%
4.3333333331
 
< 0.1%
4.569
 
0.1%
ValueCountFrequency (%)
563180
56.1%
4.569
 
0.1%
4.3333333331
 
< 0.1%
421229
 
18.8%
3.532
 
< 0.1%
3.3333333331
 
< 0.1%
39502
 
8.4%
2.548
 
< 0.1%
23941
 
3.5%
1.518
 
< 0.1%

total_payment
Real number (ℝ≥0)

Distinct28281
Distinct (%)25.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean414.6471918
Minimum22.05
Maximum31427.38
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size880.2 KiB
2021-12-17T13:53:33.916778image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum22.05
5-th percentile76.986
Q1151.04
median263.21
Q3449.4
95-th percentile1222.4
Maximum31427.38
Range31405.33
Interquartile range (IQR)298.36

Descriptive statistics

Standard deviation627.5527635
Coefficient of variation (CV)1.513461989
Kurtosis489.284683
Mean414.6471918
Median Absolute Deviation (MAD)131.01
Skewness13.87773494
Sum46708762.21
Variance393822.471
MonotonicityNot monotonic
2021-12-17T13:53:34.087879image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
178.41255
 
0.2%
80.5166
 
0.1%
168.68161
 
0.1%
268.96131
 
0.1%
130.59123
 
0.1%
149.5117
 
0.1%
247.89117
 
0.1%
356.82117
 
0.1%
229.77106
 
0.1%
155.25106
 
0.1%
Other values (28271)111248
98.8%
ValueCountFrequency (%)
22.051
< 0.1%
23.161
< 0.1%
25.051
< 0.1%
26.591
< 0.1%
26.731
< 0.1%
26.752
< 0.1%
28.111
< 0.1%
28.241
< 0.1%
28.51
< 0.1%
29.652
< 0.1%
ValueCountFrequency (%)
31427.388
< 0.1%
16732.224
< 0.1%
15937.411
 
< 0.1%
15921.081
 
< 0.1%
15471.321
 
< 0.1%
13987.546
< 0.1%
11385.781
 
< 0.1%
11061.712
 
< 0.1%
10957.981
 
< 0.1%
10768.091
 
< 0.1%

payment_type
Categorical

HIGH CARDINALITY

Distinct74
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.2 MiB
credit_card
83616 
boleto
22867 
debit_card
 
1688
voucher
 
1192
credit_card,voucher
 
1112
Other values (69)
 
2172

Length

Max length231
Median length11
Mean length10.27495628
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)< 0.1%

Sample

1st rowcredit_card
2nd rowcredit_card
3rd rowcredit_card
4th rowcredit_card
5th rowcredit_card

Common Values

ValueCountFrequency (%)
credit_card83616
74.2%
boleto22867
 
20.3%
debit_card1688
 
1.5%
voucher1192
 
1.1%
credit_card,voucher1112
 
1.0%
voucher,credit_card954
 
0.8%
credit_card,credit_card331
 
0.3%
voucher,voucher243
 
0.2%
voucher,voucher,credit_card100
 
0.1%
voucher,voucher,voucher95
 
0.1%
Other values (64)449
 
0.4%

Length

2021-12-17T13:53:34.272203image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
credit_card83616
74.2%
boleto22867
 
20.3%
debit_card1688
 
1.5%
voucher1192
 
1.1%
credit_card,voucher1112
 
1.0%
voucher,credit_card954
 
0.8%
credit_card,credit_card331
 
0.3%
voucher,voucher243
 
0.2%
voucher,voucher,credit_card100
 
0.1%
voucher,voucher,voucher95
 
0.1%
Other values (64)449
 
0.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

customer_unique_id
Categorical

HIGH CARDINALITY
UNIFORM

Distinct95419
Distinct (%)84.7%
Missing0
Missing (%)0.0%
Memory size9.6 MiB
c8460e4251689ba205045f3ea17884a1
 
24
4546caea018ad8c692964e3382debd19
 
21
698e1cf81d01a3d389d96145f7fa6df8
 
20
c402f431464c72e27330a67f7b94d4fb
 
20
0f5ac8d5c31de21d2f25e24be15bbffb
 
18
Other values (95414)
112544 

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique83551 ?
Unique (%)74.2%

Sample

1st row871766c5855e863f6eccc05f988b23cb
2nd roweb28e67c4c0b83846050ddfb8a35d051
3rd row3818d81c6709e39d06b2738a8d3a2474
4th rowaf861d436cfc08b2c2ddefd0ba074622
5th row64b576fb70d441e8f1b2d7d446e483c5

Common Values

ValueCountFrequency (%)
c8460e4251689ba205045f3ea17884a124
 
< 0.1%
4546caea018ad8c692964e3382debd1921
 
< 0.1%
698e1cf81d01a3d389d96145f7fa6df820
 
< 0.1%
c402f431464c72e27330a67f7b94d4fb20
 
< 0.1%
0f5ac8d5c31de21d2f25e24be15bbffb18
 
< 0.1%
8d50f5eadf50201ccdcedfb9e2ac845516
 
< 0.1%
eae0a83d752b1dd32697e0e7b422165615
 
< 0.1%
11f97da02237a49c8e783dfda6f50e8e15
 
< 0.1%
31e412b9fb766b6794724ed17a41dfa614
 
< 0.1%
f7ea4eef770a388bd5b225acfc54660414
 
< 0.1%
Other values (95409)112470
99.8%

Length

2021-12-17T13:53:34.423182image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
c8460e4251689ba205045f3ea17884a124
 
< 0.1%
4546caea018ad8c692964e3382debd1921
 
< 0.1%
698e1cf81d01a3d389d96145f7fa6df820
 
< 0.1%
c402f431464c72e27330a67f7b94d4fb20
 
< 0.1%
0f5ac8d5c31de21d2f25e24be15bbffb18
 
< 0.1%
8d50f5eadf50201ccdcedfb9e2ac845516
 
< 0.1%
eae0a83d752b1dd32697e0e7b422165615
 
< 0.1%
11f97da02237a49c8e783dfda6f50e8e15
 
< 0.1%
3e43e6105506432c953e165fb2acf44c14
 
< 0.1%
31e412b9fb766b6794724ed17a41dfa614
 
< 0.1%
Other values (95409)112470
99.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

customer_zip_code
Real number (ℝ≥0)

HIGH CORRELATION

Distinct14976
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35119.85556
Minimum1003
Maximum99990
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size880.2 KiB
2021-12-17T13:53:34.577228image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1003
5-th percentile3308
Q111310
median24340
Q359030
95-th percentile90598
Maximum99990
Range98987
Interquartile range (IQR)47720

Descriptive statistics

Standard deviation29866.33077
Coefficient of variation (CV)0.8504115491
Kurtosis-0.7928674712
Mean35119.85556
Median Absolute Deviation (MAD)16389
Skewness0.7801231393
Sum3956146369
Variance891997713.6
MonotonicityNot monotonic
2021-12-17T13:53:34.751002image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22790154
 
0.1%
22793151
 
0.1%
24220145
 
0.1%
24230134
 
0.1%
22775123
 
0.1%
29101113
 
0.1%
35162111
 
0.1%
11740106
 
0.1%
13212104
 
0.1%
22631104
 
0.1%
Other values (14966)111402
98.9%
ValueCountFrequency (%)
10031
 
< 0.1%
10042
 
< 0.1%
10056
< 0.1%
10062
 
< 0.1%
10074
< 0.1%
10084
< 0.1%
10098
< 0.1%
10116
< 0.1%
10122
 
< 0.1%
10133
 
< 0.1%
ValueCountFrequency (%)
999901
 
< 0.1%
999803
 
< 0.1%
999701
 
< 0.1%
999652
 
< 0.1%
999601
 
< 0.1%
999553
 
< 0.1%
999509
< 0.1%
999402
 
< 0.1%
999305
< 0.1%
999251
 
< 0.1%

customer_city
Categorical

HIGH CARDINALITY

Distinct4110
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size7.2 MiB
sao paulo
17808 
rio de janeiro
 
7837
belo horizonte
 
3144
brasilia
 
2392
curitiba
 
1751
Other values (4105)
79715 

Length

Max length32
Median length9
Mean length10.33830462
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1057 ?
Unique (%)0.9%

Sample

1st rowcampos dos goytacazes
2nd rowsanta fe do sul
3rd rowpara de minas
4th rowatibaia
5th rowvarzea paulista

Common Values

ValueCountFrequency (%)
sao paulo17808
 
15.8%
rio de janeiro7837
 
7.0%
belo horizonte3144
 
2.8%
brasilia2392
 
2.1%
curitiba1751
 
1.6%
campinas1654
 
1.5%
porto alegre1612
 
1.4%
salvador1412
 
1.3%
guarulhos1329
 
1.2%
sao bernardo do campo1060
 
0.9%
Other values (4100)72648
64.5%

Length

2021-12-17T13:53:34.922589image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sao23985
 
12.1%
paulo17887
 
9.1%
de10972
 
5.6%
rio9436
 
4.8%
janeiro7837
 
4.0%
do4868
 
2.5%
belo3214
 
1.6%
horizonte3172
 
1.6%
brasilia2402
 
1.2%
porto1915
 
1.0%
Other values (3280)111800
56.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

customer_state
Categorical

HIGH CORRELATION

Distinct27
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.3 MiB
SP
47446 
RJ
14579 
MG
13129 
RS
6235 
PR
5740 
Other values (22)
25518 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRJ
2nd rowSP
3rd rowMG
4th rowSP
5th rowSP

Common Values

ValueCountFrequency (%)
SP47446
42.1%
RJ14579
 
12.9%
MG13129
 
11.7%
RS6235
 
5.5%
PR5740
 
5.1%
SC4176
 
3.7%
BA3799
 
3.4%
DF2406
 
2.1%
GO2333
 
2.1%
ES2256
 
2.0%
Other values (17)10548
 
9.4%

Length

2021-12-17T13:53:35.062618image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sp47446
42.1%
rj14579
 
12.9%
mg13129
 
11.7%
rs6235
 
5.5%
pr5740
 
5.1%
sc4176
 
3.7%
ba3799
 
3.4%
df2406
 
2.1%
go2333
 
2.1%
es2256
 
2.0%
Other values (17)10548
 
9.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

customer_age
Real number (ℝ≥0)

Distinct72
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.5508358
Minimum14
Maximum85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size880.2 KiB
2021-12-17T13:53:35.209029image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile17
Q132
median50
Q368
95-th percentile82
Maximum85
Range71
Interquartile range (IQR)36

Descriptive statistics

Standard deviation20.81476474
Coefficient of variation (CV)0.4200688929
Kurtosis-1.201018794
Mean49.5508358
Median Absolute Deviation (MAD)18
Skewness-0.001716514946
Sum5581753
Variance433.254431
MonotonicityNot monotonic
2021-12-17T13:53:35.382890image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
321659
 
1.5%
141654
 
1.5%
501643
 
1.5%
291634
 
1.5%
831634
 
1.5%
711625
 
1.4%
551620
 
1.4%
571615
 
1.4%
671613
 
1.4%
361613
 
1.4%
Other values (62)96337
85.5%
ValueCountFrequency (%)
141654
1.5%
151575
1.4%
161544
1.4%
171565
1.4%
181531
1.4%
191556
1.4%
201553
1.4%
211503
1.3%
221592
1.4%
231570
1.4%
ValueCountFrequency (%)
851597
1.4%
841587
1.4%
831634
1.5%
821611
1.4%
811574
1.4%
801559
1.4%
791563
1.4%
781502
1.3%
771592
1.4%
761577
1.4%

customer_income
Real number (ℝ≥0)

Distinct35159
Distinct (%)31.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20924.35273
Minimum2000
Maximum40000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size880.2 KiB
2021-12-17T13:53:35.563295image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2000
5-th percentile3860
Q111476
median20867
Q330441.5
95-th percentile38055.7
Maximum40000
Range38000
Interquartile range (IQR)18965.5

Descriptive statistics

Standard deviation10959.11792
Coefficient of variation (CV)0.5237494352
Kurtosis-1.198209257
Mean20924.35273
Median Absolute Deviation (MAD)9474
Skewness0.006106550577
Sum2357065562
Variance120102265.7
MonotonicityNot monotonic
2021-12-17T13:53:35.737499image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
770321
 
< 0.1%
2373321
 
< 0.1%
1151620
 
< 0.1%
201418
 
< 0.1%
561018
 
< 0.1%
1711518
 
< 0.1%
1509817
 
< 0.1%
2465116
 
< 0.1%
3926816
 
< 0.1%
2761916
 
< 0.1%
Other values (35149)112466
99.8%
ValueCountFrequency (%)
20002
 
< 0.1%
20011
 
< 0.1%
20023
 
< 0.1%
20033
 
< 0.1%
20043
 
< 0.1%
20062
 
< 0.1%
20071
 
< 0.1%
20082
 
< 0.1%
20098
< 0.1%
20103
 
< 0.1%
ValueCountFrequency (%)
400005
< 0.1%
399992
 
< 0.1%
399981
 
< 0.1%
399974
< 0.1%
399967
< 0.1%
399951
 
< 0.1%
399946
< 0.1%
399921
 
< 0.1%
399912
 
< 0.1%
399903
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.2 MiB
0
56395 
1
56252 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
056395
50.1%
156252
49.9%

Length

2021-12-17T13:53:35.902003image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-17T13:53:35.985392image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
056395
50.1%
156252
49.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

order_year
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.6 MiB
2018
61416 
2017
50864 
2016
 
367

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2017
2nd row2017
3rd row2018
4th row2018
5th row2017

Common Values

ValueCountFrequency (%)
201861416
54.5%
201750864
45.2%
2016367
 
0.3%

Length

2021-12-17T13:53:36.069816image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-17T13:53:36.153823image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
201861416
54.5%
201750864
45.2%
2016367
 
0.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

order_month
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.02675615
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size880.2 KiB
2021-12-17T13:53:36.240544image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q38
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.232548014
Coefficient of variation (CV)0.5363661534
Kurtosis-0.9806750194
Mean6.02675615
Median Absolute Deviation (MAD)2
Skewness0.2084695862
Sum678896
Variance10.44936666
MonotonicityNot monotonic
2021-12-17T13:53:36.361414image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
812158
10.8%
512061
10.7%
711611
10.3%
311217
10.0%
610661
9.5%
410659
9.5%
29623
8.5%
19163
8.1%
118665
7.7%
126309
5.6%
Other values (2)10520
9.3%
ValueCountFrequency (%)
19163
8.1%
29623
8.5%
311217
10.0%
410659
9.5%
512061
10.7%
610661
9.5%
711611
10.3%
812158
10.8%
94835
 
4.3%
105685
5.0%
ValueCountFrequency (%)
126309
5.6%
118665
7.7%
105685
5.0%
94835
 
4.3%
812158
10.8%
711611
10.3%
610661
9.5%
512061
10.7%
410659
9.5%
311217
10.0%

order_weekday
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.9 MiB
Monday
18393 
Tuesday
18237 
Wednesday
17600 
Thursday
16794 
Friday
16039 
Other values (2)
25584 

Length

Max length9
Median length7
Mean length7.144824097
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWednesday
2nd rowWednesday
3rd rowSunday
4th rowWednesday
5th rowSaturday

Common Values

ValueCountFrequency (%)
Monday18393
16.3%
Tuesday18237
16.2%
Wednesday17600
15.6%
Thursday16794
14.9%
Friday16039
14.2%
Sunday13416
11.9%
Saturday12168
10.8%

Length

2021-12-17T13:53:36.499419image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-17T13:53:36.598165image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
monday18393
16.3%
tuesday18237
16.2%
wednesday17600
15.6%
thursday16794
14.9%
friday16039
14.2%
sunday13416
11.9%
saturday12168
10.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2021-12-17T13:53:25.559589image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T13:53:10.571989image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T13:53:12.385002image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T13:53:14.236019image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T13:53:16.212347image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T13:53:18.078479image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T13:53:19.935418image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T13:53:21.907663image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T13:53:23.715783image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T13:53:25.747987image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T13:53:10.793507image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T13:53:12.577774image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T13:53:14.427516image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T13:53:16.406342image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T13:53:18.293617image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T13:53:20.119633image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T13:53:22.102354image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T13:53:23.913001image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T13:53:25.952836image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T13:53:10.988346image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T13:53:12.790066image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T13:53:14.804200image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T13:53:16.631329image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T13:53:18.495259image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T13:53:20.321376image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T13:53:22.312364image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T13:53:24.120699image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T13:53:26.191771image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T13:53:11.173758image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T13:53:12.992237image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T13:53:14.997556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T13:53:16.838460image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T13:53:18.691534image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T13:53:20.517625image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T13:53:22.505196image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T13:53:24.319287image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T13:53:26.401275image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T13:53:11.380680image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T13:53:13.204676image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T13:53:15.210715image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T13:53:17.045360image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T13:53:18.926277image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T13:53:20.743602image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T13:53:22.709657image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T13:53:24.531024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T13:53:26.602083image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T13:53:11.586155image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T13:53:13.410169image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T13:53:15.423685image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T13:53:17.249937image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T13:53:19.133278image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T13:53:20.937760image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T13:53:22.904583image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T13:53:24.740155image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T13:53:26.795773image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T13:53:11.800453image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T13:53:13.613849image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T13:53:15.612590image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T13:53:17.441983image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T13:53:19.327854image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T13:53:21.142321image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T13:53:23.099431image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T13:53:24.941602image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T13:53:26.993426image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T13:53:11.996090image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T13:53:13.810222image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T13:53:15.811847image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T13:53:17.652846image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T13:53:19.525544image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T13:53:21.338817image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T13:53:23.304940image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T13:53:25.148699image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T13:53:27.188744image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T13:53:12.188769image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T13:53:14.043060image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T13:53:16.022503image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T13:53:17.864340image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T13:53:19.736794image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T13:53:21.555520image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T13:53:23.514341image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-17T13:53:25.362517image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2021-12-17T13:53:36.725864image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-12-17T13:53:36.948007image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-12-17T13:53:37.173080image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-12-17T13:53:37.385665image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2021-12-17T13:53:37.590271image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-12-17T13:53:27.927824image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2021-12-17T13:53:29.239176image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexorder_idorder_item_idproduct_idseller_idseller_zip_codeseller_cityseller_stateproduct_category_name_englishcustomer_idorder_statusorder_dateorder_delivery_dateorder_estimated_delivery_datereview_scoretotal_paymentpayment_typecustomer_unique_idcustomer_zip_codecustomer_citycustomer_statecustomer_agecustomer_incomecustomer_marital_statusorder_yearorder_monthorder_weekday
0000010242fe8c5a6d1ba2dd792cb1621414244733e06e7ecb4970a6e2683c13e6148436dade18ac8b2bce089ec2a04120227277volta redondaSPcool_stuff3ce436f183e68e07877b285a838db11adelivered2017-09-13 08:59:022017-09-20 23:43:482017-09-29 00:00:005.0166.04credit_card871766c5855e863f6eccc05f988b23cb28013campos dos goytacazesRJ777208020179Wednesday
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Last rows

df_indexorder_idorder_item_idproduct_idseller_idseller_zip_codeseller_cityseller_stateproduct_category_name_englishcustomer_idorder_statusorder_dateorder_delivery_dateorder_estimated_delivery_datereview_scoretotal_paymentpayment_typecustomer_unique_idcustomer_zip_codecustomer_citycustomer_statecustomer_agecustomer_incomecustomer_marital_statusorder_yearorder_monthorder_weekday
112637112640fffb9224b6fc7c43ebb0904318b10b5f143423cdffde7fda63d0414ed38c11a73b1fc4f64df5a0e8b6913ab38803c57a924440sao goncaloRJwatches_gifts4d3abb73ceb86353aeadbe698aa9d5cbdelivered2017-10-27 16:51:002017-11-17 19:41:422017-11-27 00:00:004.0820.55boletof736308cd9952b33b90b9fe94da9c8f556912serra talhadaPE1479421201710Friday
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112641112644fffbee3b5462987e66fb49b1c5411df216f0169f259bb0ff432bfff7d829b9946213b25e6f54661939f11710a6fddb87113321saltoSPhome_construction11a0e041ea6e7e21856d2689b64e7f3adelivered2018-06-19 09:27:482018-07-05 17:51:082018-07-23 00:00:005.0321.72credit_cardecc3d4eb9b17d2f0865d21f2abecc51c39401montes clarosMG2118335120186Tuesday
112642112645fffc94f6ce00a00581880bf54a75a03714aa6014eceb682077f9dc4bffebc05b0b8bc237ba3788b23da09c0f1f3a3288c88303itajaiSChousewaresb51593916b4b8e0d6f66f2ae24f2673ddelivered2018-04-23 13:57:062018-05-10 22:56:402018-05-18 00:00:005.0789.82boleto0c9aeda10a71f369396d0c04dce13a6465077sao luisMA3522497020184Monday
112643112646fffcd46ef2263f404302a634eb57f7eb132e07fd915822b0765e448c4dd74c828f3c38ab652836d21de61fb8314b691821206sao pauloSPcomputers_accessories84c5d4fbaf120aae381fad077416eaa0delivered2018-07-14 10:26:462018-07-23 20:31:552018-08-01 00:00:005.0889.02boleto0da9fe112eae0c74d3ba1fe16de0988b81690curitibaPR3627170120187Saturday
112644112647fffce4705a9662cd70adb13d4a31832d172a30483855e2eafc67aee5dc2560482c3cfdc648177fdbbbb35635a37472c5380610curitibaPRsports_leisure29309aa813182aaddc9b259e31b870e6delivered2017-10-23 17:07:562017-10-28 12:22:222017-11-10 00:00:005.0268.76credit_cardcd79b407828f02fdbba457111c38e4c44039sao pauloSP61373571201710Monday
112645112648fffe18544ffabc95dfada21779c9644f19c422a519119dcad7575db5af1ba540e2b3e4a2a3ea8e01938cabda2a3e5cc794733sao pauloSPcomputers_accessoriesb5e6afd5a41800fdf401e0272ca74655delivered2017-08-14 23:02:592017-08-16 21:59:402017-08-25 00:00:005.0148.83credit_cardeb803377c9315b564bdedad67203930613289vinhedoSP673786020178Monday
112646112649fffe41c64501cc87c801fd61db3f62441350688d9dc1e75ff97be326363655e01f7ccf836d21b2fb1de37564105216cc114940ibitingaSPbed_bath_table96d649da0cc4ff33bb408b199d4c7dcfdelivered2018-06-09 17:00:182018-06-14 17:56:262018-06-28 00:00:005.0128.32credit_cardcd76a00d8e3ca5e6ab9ed9ecb6667ac418605botucatuSP5839665120186Saturday